Provides functions to apply pooling or backward selection
of logistic, Cox regression and Multilevel (mixed models) prediction
models in multiply imputed datasets. Backward selection can be done
from the pooled model using Rubin's Rules (RR), the D1, D2, D3 and
promising median p-values method. The model can contain
continuous, dichotomous, categorical predictors and interaction terms
between all these type of predictors. Continuous predictors can also
be introduced as restricted cubic spline coefficients. It is also possible
to force (spline) predictors or interaction terms in the model during predictor
selection. The package includes a function to evaluate the stability
of the models using bootstrapping and cluster bootstrapping. The package further
contains functions to generate pooled model performance measures in multiply
imputed datasets as ROC/AUC, R-squares, Brier score, fit test values and
calibration plots for logistic regression models. A function to apply
Bootstrap internal validation is also available where two methods can be
used to combine bootstrapping and multiple imputation. One method, boot_MI,
first draws bootstrap samples and subsequently performs multiple imputation and with
the other method, MI_boot, first bootstrap samples are drawn from each imputed
dataset before results are combined. The adjusted intercept after shrinkage of
the pooled regression coefficients can be subsequently obtained. Backward selection
as part of internal validation is also an option. Also a function to externally
validate logistic prediction models in multiple imputed datasets is available.
Eekhout (2017)